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Privacy for Location-based Services [electronic resource] / by Gabriel Ghinita.

By: Ghinita, Gabriel [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Information Security, Privacy, and Trust: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2013Edition: 1st ed. 2013.Description: XI, 73 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031023361.Subject(s): Data protection | Data and Information SecurityAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.8 Online resources: Click here to access online
Contents:
Introduction -- Privacy-Preserving Spatial Transformations -- Cryptographic Approaches -- Hybrid Approaches -- Private Matching of Spatial Datasets -- Trajectory Anonymization -- Differentially Private Publication of Spatial Datasets -- Conclusions.
In: Springer Nature eBookSummary: Sharing of location data enables numerous exciting applications, such as location-based queries, location-based social recommendations, monitoring of traffic and air pollution levels, etc. Disclosing exact user locations raises serious privacy concerns, as locations may give away sensitive information about individuals' health status, alternative lifestyles, political and religious affiliations, etc. Preserving location privacy is an essential requirement towards the successful deployment of location-based applications. These lecture notes provide an overview of the state-of-the-art in location privacy protection. A diverse body of solutions is reviewed, including methods that use location generalization, cryptographic techniques or differential privacy. The most prominent results are discussed, and promising directions for future work are identified.
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Introduction -- Privacy-Preserving Spatial Transformations -- Cryptographic Approaches -- Hybrid Approaches -- Private Matching of Spatial Datasets -- Trajectory Anonymization -- Differentially Private Publication of Spatial Datasets -- Conclusions.

Sharing of location data enables numerous exciting applications, such as location-based queries, location-based social recommendations, monitoring of traffic and air pollution levels, etc. Disclosing exact user locations raises serious privacy concerns, as locations may give away sensitive information about individuals' health status, alternative lifestyles, political and religious affiliations, etc. Preserving location privacy is an essential requirement towards the successful deployment of location-based applications. These lecture notes provide an overview of the state-of-the-art in location privacy protection. A diverse body of solutions is reviewed, including methods that use location generalization, cryptographic techniques or differential privacy. The most prominent results are discussed, and promising directions for future work are identified.

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